spyglass VS tantivy

No direct experience but I saved some links about this:

https://wiby.me/about/guide.html

https://github.com/spyglass-search/spyglass

https://yacy.net/

Interesting thread(s) on HN about the last one:

https://news.ycombinator.com/item?id=32597309

I've been working on a self-hosted personal search app (https://github.com/spyglass-search/spyglass) and have recently been playing around with how to integrate it with local LLMs. I think this would be an awesome step into having your own personal assistant that can search through all your data and give you analysis / summaries.

We have an open-source desktop app (https://github.com/spyglass-search/spyglass) and are working on a web version that has more powerful features such as conversation search, check it out!

I'm one of the devs for Spyglass ([https://github.com/spyglass-search/spyglass](https://github.com/spyglass-search/spyglass)) an open-source personal search app. We're excited about all the advancements with language models recently and wanted to try merging the two ideas together to form something even better.

Another more recently available option is spyglass; it is more tenable than YaCy but dev is mostly on MacOS with focus on a desktop interface. I like the idea of web based interface.

I'm part of small team that's been building an open-source personal search engine (https://github.com/spyglass-search/spyglass). One of the major use cases is searching your local files & their contents which aligns perfectly with how Obsidian stores it notes.

I'm one of the developers of Spyglass (https://github.com/spyglass-search/spyglass), an open-source self-hosted personal search engine. We recently added the ability to search through your Reddit saved & upvoted posts!

Tantivy - a full-text indexing library written in Rust. Has a great performance and featureset.

By this I presume you mean build a search index that can retrieve results based on keywords? I know certain databases use Lucene to build a keyword-based index on top of unstructured blobs of data. Another alternative is to use Tantivy (https://github.com/quickwit-oss/tantivy), a Rust version of Lucene, if building search indices via Java isn't your cup of tea :)

Both libraries offer multilingual support for keywords, I believe, so that's a benefit to vector search where multilingual embedding models are rather expensive.

We also implemented our schemaless columnar storage optimized for object storage.

The inverted index and columnar storage are part of tantivy [0], which is the fastest search library out there. We maintain it and we decided to build the distributed engine on top of it.

[0] tantivy github repo: https://github.com/quickwit-oss/tantivy

The issue for geo search is here: https://github.com/quickwit-oss/tantivy/issues/44

Search index : Custom-built using tantivy.

The roaring bitmap variant is used only for the optional index (1 docid => 0 or 1 value) in the columnar storage (DocValues), not for the inverted index. Since this is used for aggregation, some queries may be a full scan.

The inverted index in tantivy uses bitpacked values of 128 elements with a skip index on top.

> I didn't follow the rest of your comment, select is what EF is good at, every other data structure needs a lot more scanning once you land on the right chunk. With BMI2 you can also use the PDEP instruction to accelerate the final select on a 64-bit block

The select for the sparse codec is a [simple array index access](https://github.com/quickwit-oss/tantivy/blob/main/columnar/s...), that is hard to beat. Compression is not good near the 5k threshold though.

Hi /r/rust, I’m a SWE on Etsy’s Retrieval Systems team where we’re building a platform based on rust and tantivy (https://github.com/quickwit-oss/tantivy). We’re looking to bring two new engineers onto the team.

Quick Search: Easily find specific notes with Velo's fuzzy-search feature, powered by tantivy. tantivy might have been a little overkill, but it was really easy to integrate.